PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Kisilevich S., Mansmann F., Nanni M., Rinzivillo S. Spatio-temporal clustering. vol. 6 Oded Maimon, Lior Rokach. (Springer Science+Business Media). New York, USA: Springer, 2010.
 
 
Abstract
(English)
Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.
URL: http://www.springerlink.com/content/m32658456701p668/fulltext.pdf
DOI: 10.1007/978-0-387-09823-4_44
Subject Spatio-temporal clustering
Trajectory clustering
I.5.3 Clustering
62-07 Data analysis


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